Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929603437658112 |
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| author | Rahman, Maxx Richard Liu, Ruoxuan Maass, Wolfgang |
| author_facet | Rahman, Maxx Richard Liu, Ruoxuan Maass, Wolfgang |
| contents | Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical domain knowledge into LLMs to improve their ability to detect anomalies. We introduce the Metabolism Pathway-driven Prompting (MPP) method, which integrates the information about metabolic pathways to better capture the structural and temporal changes in biological samples. We applied our method for doping detection in sports, focusing on steroid metabolism, and evaluated using real-world data from athletes. The results show that our method improves anomaly detection performance by leveraging metabolic context, providing a more nuanced and accurate prediction of suspicious samples in athletes' profiles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series Rahman, Maxx Richard Liu, Ruoxuan Maass, Wolfgang Quantitative Methods Artificial Intelligence Machine Learning Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this paper, we propose a targeted method that incorporates the clinical domain knowledge into LLMs to improve their ability to detect anomalies. We introduce the Metabolism Pathway-driven Prompting (MPP) method, which integrates the information about metabolic pathways to better capture the structural and temporal changes in biological samples. We applied our method for doping detection in sports, focusing on steroid metabolism, and evaluated using real-world data from athletes. The results show that our method improves anomaly detection performance by leveraging metabolic context, providing a more nuanced and accurate prediction of suspicious samples in athletes' profiles. |
| title | Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.12830 |